Enhancing Gravitational-Wave Science with Machine Learning
May 7, 202038 pages
Published in:
- Mach.Learn.Sci.Tech. 2 (2021) 1, 011002
e-Print:
- 2005.03745 [astro-ph.HE]
DOI:
- 10.1088/2632-2153/abb93a (publication)
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Abstract: (arXiv)
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future gravitational-wave detectors.- gravitational radiation
- gravitational radiation: emission
- gravitational radiation detector
- gravitational radiation: direct detection
- data analysis method
- numerical methods
- numerical calculations
- statistical analysis
- detector: sensitivity
- detector: noise
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